title: Avoiding asymmetry-induced bias in longitudinal image processing.
abstract: Longitudinal image processing procedures frequently transfer or pool information across time within subject, with the dual goals of reducing the variability and increasing the accuracy of the derived measures. In this note, we discuss common difficulties in longitudinal image processing, focusing on the introduction of bias, and describe the approaches we have taken to avoid them in the FreeSurfer longitudinal processing stream.
authors.affiliation: Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA; MIT Computer Science and AI Lab, Cambridge, MA, USA. Electronic address: mreuter@nmr.mgh.harvard.edu.
authors.forename: Martin
authors.initials: M
authors.lastname: Reuter
authors.affiliation: Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA; MIT Computer Science and AI Lab, Cambridge, MA, USA. Electronic address: fischl@nmr.mgh.harvard.edu.
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
mesh_terms: D015982:Bias; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008137:Longitudinal Studies / Q000379:methods*